Fast hierarchical optimization algorithm for multi-UAV-BS network for dynamic users

In response to dynamic user requirements, unmanned aerial vehicles can act as airborne base stations (UAV-BSs) and adopt real-time position adjustment tactics to offer access services. Nevertheless, the frequent movements of UAV-BSs heighten the complexity of network adjustments and diminish battery durability. To tackle this challenge, this paper proposes a fast hierarchical optimization algorithm. By jointly optimizing frequency allocation, power optimization, and position deployment, the algorithm intends to maximize the number of users receiving high-quality-of-service (QoS) communications. When real-time monitoring detects that the user's communication quality falls below the threshold, the algorithm triggers optimization of power and frequency parameters. To solve the non-convex optimization problem, the Block Coordinate Descent (BCD) algorithm and Genetic Algorithm (GA) are employed in an alternating computational process to obtain optimal solution. If a portion of users has not been restored above the threshold after adjusting these parameters, the re-deployment of the positions of the multi-UAV-BS will be triggered, and all parameters will be re-optimized. Simulation results show that the proposed algorithm not only presents robust convergence but also achieves a 95.5 % user coverage rate. Compared with single-objective optimization methods, the proposed algorithm shows superior performance.